Is machine learning and data analytics only for companies that have “big data”?

Is machine learning and data analytics only for companies that have “big data”?

In the last couple of weeks we talked to many people about data science and machine learning. We have been going to many public events and learned a lot on how people see data science while thinking of their organisation/business. It was amazing to see how involved and interested everyone is to the concept of machine learning trough these talks. Although it is still a very new field, we like to outline and address three of the most frequent talking points we have experienced in the last couple of weeks.

1. Data science is only useful for big companies with “big data” and a large data infrastructure.

Let’s take a step back and look at the term data science. Data science is all about translating raw data into valuable information trough building scientific models. A big part of data science is machine learning, which trains data and thus can predict future outcomes related to that data. Although it is easy to gather much more insights on “big data”, it is certainly as valuable to get insights from a small dataset (see our previous article). To us, machine learning is all about adapting and knowing which data to use for what purpose. Most organisations and business store their data in some way. Often they store only fractions of the data they should collect, and will save this data in many locations and formats. A part of the job of a data scientist is to streamline this process. Through this, organisations are able to get valuable information based on this data. For this reason, we focus big on getting organisations ready for proper use of data. We do this through ourdata analytics services. Wouldn’t it be nice to have data speaking to you in a way you can act upon to increase profit, decrease costs or improve processes?

In essence, most SMB’s could use data science to gain a competitive advantage through understanding their customers and business processes. An example of this could be what kind of customers are likely to leave a business or organisation. Based on trained signals through the data, a strategy could be developed to refrain them from leaving. Another example is product recommendation. Webshops have data on their products and machine learning trains those products to produce a recommendation model. Think about Amazon and their book recommendations as displayed below. This will further improve customer sales and satisfaction, as tailored products are shown prior to the customer’s needs. Another example known in data science is customer satisfaction. Customer satisfaction can for example be improved through natural language processing, which is a part of machine learning that focuses on text and retrieves valuable information for customer needs. All these examples are user cases for machine learning applications that will help you stay ahead of the competition.

A recommendation system based on machine learning.

3. Machine learning is something that is “scary” and “out of reach”.

We are very much aware of the complexity of machine learning and understanding the use of it. In all cases, we believe in putting the user first. In this way, you are in control of your data and what happens to it. A common question that we get asked is: are machines going to decide what I am going to do with my business? Our aim is to let data help you make decisions and improve current processes and not replace your decision making. We let data science “speak” to you, so that decision making and handling growth becomes easier. Data science is not out of reach. It is here, right in front of you, so don’t be afraid to start with your data right away.

All in all, we believe it is never too soon for your organisation to begin using data science. Whether you are preparing for growth by designing and implementing a data strategy, or trying to use the ‘small’ data you have right now, working with data scientists will make sure you that you get the most valuable information from your data. Thanks for reading!